Clinical Classification of MAFLD Based Liver Biopsy

Clinical Classification and Prediction System of MAFLD Based on Liver Biopsy

Metabolic dysfunction-associated Fatty Liver Disease (MAFLD), also known as Non-Alcoholic Fatty Liver Disease (NAFLD), is the most common chronic progressive liver disease in China. It is closely related to the high incidence of cardiovascular-renal-metabolic syndrome and both liver and non-liver malignancies, posing a serious threat to public health. However, the diagnostic criteria for MAFLD are not unified globally, and the classification and staging still rely on liver biopsy for pathological assessment. The characteristics, mechanisms, and predictive indicators of liver and extrahepatic disease outcomes in MAFLD patients are not yet clear.

The severe form of MAFLD, metabolic dysfunction-associated steatohepatitis (MASH), has been a hot and challenging area of research for non-invasive tests (NITs). However, serum markers, imaging examinations, and novel markers under development cannot replace liver biopsy for the diagnosis of MASH. Clinically, the disease outcomes of MAFLD mainly depend on metabolic cardiovascular risk factors and fibrosis staging. Both liver biopsy and NIT-diagnosed advanced fibrosis and cirrhosis can predict liver-related events and all-cause mortality risks in MAFLD patients. Artificial intelligence and machine learning methods can improve the consistency of pathologists in diagnosing MASH and fibrosis. The Agile score, which combines gender, T2DM status, AST/ALT ratio, platelet count, and liver stiffness measurement (LSM), can improve the diagnostic efficacy of advanced fibrosis and cirrhosis in MAFLD patients and the efficiency of predicting liver-related events. However, the predictive effect of fibrosis staging and its changes on liver cancer needs to be improved. There is a lack of high-quality research on early warning indicators for the incidence of CVD, chronic kidney disease, and non-liver malignancies in MAFLD patients. It is necessary to explore the role of conventional indicators such as low-density lipoprotein cholesterol, lipoprotein(a), uric acid, and high-sensitivity C-reactive protein, as well as multi-omics parameters, in the classification, staging, and risk prediction of MAFLD.

MAFLD is an increasingly serious public health issue associated with a higher risk of liver-related events, cardiovascular-renal-metabolic syndrome, and malignancies. The prevalence of MAFLD in China is high, but the rate of standardized management is low. Even patients with the same classification and staging often have different clinical characteristics and outcomes. There is currently a lack of a clinical classification and early warning system for MAFLD that combines metabolic cardiovascular risk factors and NITs for different outcome risks.

Study Overview

• Status: Not yet recruiting
• Conditions: Metabolic Dysfunction-associated Steatohepatitis (MASH); Metabolic Dysfunction Associated Steatotic Liver Disease

Detailed Description

1. Recruitment and Data Collection:

   On the basis of an existing cohort of 1,500 liver biopsy cases, recruit an additional 500 cases from a national multicenter liver biopsy follow-up cohort (totaling 2,000 cases). Collect demographic, anthropometric, laboratory, imaging, and liver biopsy results for these patients.

   Concurrently, biological samples, including blood, urine, feces, and liver biopsy tissues, will be collected. Utilize these samples to perform quantitative metabolite information based on database matching. Employ techniques such as genomics, epigenomics, proteomics, metabolomics, immunomics, and microbiome metagenomics to screen for differential biomarkers across different subgroups.

   Combine these findings with clinical and imaging parameters of MAFLD patients to analyze and explore key parameters and molecules at different stages and outcomes of MAFLD disease progression.

2. Development and Validation of a Diagnostic and Prognostic System:

Based on key molecules identified through multi-omics, in conjunction with characteristic parameters from clinical and imaging data of MAFLD patients, use machine learning methods (such as random forests neural networks) combined with logistic regression to establish a novel non-invasive diagnostic and prognostic assessment system for adverse outcomes (cardiovascular events, non-liver malignancies, and liver-related events).

Validate this new assessment system to ensure its reliability and accuracy in predicting disease outcomes.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Jingjie Zhao, M.D.; Phone Number: 01063138328; Email: zhaojj@ccmu.edu.cn

Study Locations

• China
  • Shanghai, China
    • Xinhua Hospital, Shanghai Jiaotong University School of Medicine
    • Contact: Ruixu Yang; Phone Number: +8615000600658; Email: fattyliver2004@126.com
    • Contact: Jiangao Fan
  • Shanghai, China
    • Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine
    • Contact: Qing Xie
    • Contact: Xiaolin Wang; Phone Number: +8613916785394; Email: visit-12345@hotmail.com
  • Tianjin, China
    • Tianjin Second People's Hospital
    • Contact: Liang Xu
    • Contact: Huan Liu; Phone Number: +8613622105240; Email: 13622105240@126.com
  • Xicheng
    • Beijing, Xicheng, China, 100050
      • Beijing Friendship Hospital, Capital Medical University
      • Contact: Jingjie Zhao; Phone Number: 01063138328; Email: zhaojj@ccmu.edu.cn
      • Contact: Hong You
    • Hangzhou, Xicheng, China, 100050
      • Hangzhou Normal University Affiliated Hospital
      • Contact: Junping Shi
      • Contact: Chunlan Liu; Phone Number: +8618325321386; Email: liuchunlan2025@163.com
    • Shanghai, Xicheng, China, 100050
      • Zhongshan Hospital, Fudan University
      • Contact: Xinyu Yang; Phone Number: +8618800332829; Email: yangxy1105@163.com
      • Contact: Hua Bian

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

MAFLD patients diagnosis by liver biopsy

Description

Inclusion Criteria:

• Ultrasound confirmation of fatty liver and the presence of at least one of the following metabolic cardiovascular risk factors:

  1. BMI ≥ 24 kg/m² or waist circumference ≥ 90 cm (men) and 85 cm (women) or excessive body fat content and body fat percentage.
  2. Fasting blood glucose ≥ 6.1 mmol/L or 2-hour post-load blood glucose ≥ 7.8 mmol/L or HbA1c ≥ 5.7% or history of Type 2 Diabetes Mellitus (T2DM) or Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) ≥ 2.5.
  3. Fasting serum triglycerides ≥ 1.70 mmol/L or currently receiving lipid-lowering drug therapy.
  4. Serum high-density lipoprotein cholesterol (HDL-C) ≤ 1.0 mmol/L (men) and 1.3 mmol/L (women) or currently receiving lipid-lowering drug therapy.
  5. Blood pressure ≥ 130/85 mmHg or currently receiving antihypertensive drug therapy.
• histology of liver-biopsy

Exclusion Criteria:

• Excessive Alcohol Consumption: Individuals who consume alcohol equivalent to ≥30 grams of ethanol per day for males, or ≥20 grams of ethanol per day for females, or those with missing alcohol consumption information.
• Viral Hepatitis Markers: Individuals who are positive for hepatitis B surface antigen (HBsAg), positive for hepatitis C virus (HCV) antibodies, or have missing information regarding these markers.
• History of Serious Medical Conditions: Individuals with a history of malignant tumors, cardiovascular diseases, chronic kidney disease, decompensated liver cirrhosis (manifested by ascites, gastrointestinal bleeding, hepatic encephalopathy, hepatorenal syndrome, etc.), or those who have undergone liver transplantation.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
MAFLD diagnosed by liver-biopsy; retrospective and prospective cohort about MAFLD diagnosed by liver-biopsy

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
composite endpoint	Number of participants with the composite endpoint, including A. Liver-Related Events: Cirrhosis Liver decompensation Hepatocellular carcinoma Liver transplantation B. Metabolic Diseases: Type 2 Diabetes Mellitus (T2DM) Hypertension Dyslipidemia Gout C. Cardiovascular Diseases (CVD): Coronary heart disease Stroke Heart failure Atrial fibrillation D. Non-Liver Malignancies: Colorectal adenoma/adenocarcinoma Gastric cancer Esophageal cancer Pancreatic cancer Gallbladder cancer Lung cancer Prostate cancer Hematological malignancies E. Chronic Kidney Disease F. Mortality: Liver disease-related deaths Cardiovascular disease-related deaths Other causes of death	1-20 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Liver-Related Events	Number of participants with liver-related events, including Cirrhosis, Liver decompensation, Hepatocellular carcinoma, Liver transplantation	1-20 years
Metabolic Diseases	Number of participants with metabolic disease, including Type 2 Diabetes Mellitus (T2DM), Hypertension, Dyslipidemia, Gout	1-20 years
Cardiovascular Diseases (CVD)	Number of participants with CVD, including Coronary heart disease, Stroke, Heart failure, Atrial fibrillation	1-20 years
Non-Liver Tumors	Number of participants with non-liver tumors, including colorectal adenoma/adenocarcinoma, gastric cancer, esophageal cancer, pancreatic cancer, gallbladder cancer, lung cancer, prostate cancer, hematological malignancies and so on	1-20 years
Chronic Kidney Disease	Number of participants with chronic kidney disease	1-20 years
Mortality	Number of participants including liver disease-related deaths, cardiovascular disease-related deaths, and Other causes of death	1-20 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
biomarker by proteomics	the clinical classification of MASLD by proteomics	baseline
biomarker by metabolomics	the clinical classification of MASLD by metabolomics	baseline
biomarker by Immunomics	the clinical classification of MASLD by immunomics	baseline
biomarker by Microbial Metagenomics	the clinical classification of MASLD by microbial metagenomics	baseline

Collaborators and Investigators

• Sponsor: Beijing Friendship Hospital
• Collaborators: Fudan University; Ruijin Hospital; Xinhua Hospital, Shanghai Jiao Tong University School of Medicine; Tianjin Second People's Hospital; The Affiliated Hospital of Hangzhou Normal University
• Investigators: Principal Investigator: Hong You, PhD. / M.D., Beijing Friendship Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): February 8, 2025
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2038

Study Registration Dates

• First Submitted: January 20, 2025
• First Submitted That Met QC Criteria: January 25, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 8, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: classification; prediction model; MAFLD; MASLD; liver-biopsy
• Additional Relevant MeSH Terms: Digestive System Diseases; Liver Diseases; Fatty Liver
• Other Study ID Numbers: 2023ZD0508702 (Other Grant/Funding Number: Noncommunicable Chronic Diseases-National Science and Technology Major Project)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Patient Demographics, Anthropometrics, Serological, and Imaging Data, and Liver Biopsy Scores
• IPD Sharing Time Frame: After the project finished
• IPD Sharing Access Criteria: ask the principle investigator
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No